Accelerated Life Testing Data-Based Lifetime Prediction of Rubber Material for Bushing Using Global-Local Optimization Technique

Abstract

Objective: This research aims to devise a predictive method for assessing the lifespan and dependability of rubber materials employed in automotive suspension systems. This study intends to improve product dependability, and design verification by evaluating the degradation patterns of these materials under accelerated testing.BRMethods: An accelerated life test was conducted, subjecting materials to different temperature and time conditions. Degradation behavior was determined by applying global-local optimization techniques and selecting an appropriate degradation model. The Arrhenius equation (k = A·exp (−ESUBa/SUB/RT)), was used in regression analysis to forecast the lifespan of the material under typical temperature conditions.BRResults: The model’s reliability is assessed through the coefficient of determination and mean squared error. As optimization advances, predictions become more reliable and are confirmed by a coefficient of determination exceeding a 99% confidence level.BRConclusion: Predictive approaches for estimating the lifespan and dependability of rubber materials in automotive suspension systems facilitate early-stage validation, enhancing product reliability and, minimizing failure expenses.